92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
import uuid
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from typing import Any, Dict, List
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from langchain_openai import ChatOpenAI
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from langchain.agents import create_agent
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from langchain.agents.middleware import HumanInTheLoopMiddleware
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.types import Command
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from langchain.tools import tool
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def main() -> None:
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llm = ChatOpenAI(
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model="gpt-3.5-turbo",
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temperature=0,
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# For a local model, uncomment and adjust:
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# base_url="http://localhost:11434/v1",
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# api_key="ollama",
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)
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@tool("get_weather")
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def get_weather(city: str, date: str) -> str:
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"""Return a mocked weather string for the given city and date."""
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return f"Weather in {city} on {date}"
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tools = [get_weather]
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memory = MemorySaver()
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agent = create_agent(
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model=llm,
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tools=tools,
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system_prompt="You are a helpful assistant",
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middleware=[
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HumanInTheLoopMiddleware(
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interrupt_on={"get_weather": True},
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description_prefix="Confirm tool usage:",
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),
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],
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checkpointer=memory,
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)
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thread_id = str(uuid.uuid4())
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config: Dict[str, Any] = {"configurable": {"thread_id": thread_id}}
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user_message = input("Enter your question: ")
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result = agent.invoke(
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{"messages": [{"role": "human", "content": user_message}]},
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config=config,
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)
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while "__interrupt__" in result:
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interrupt_value = result["__interrupt__"][0].value
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action_requests = interrupt_value["action_requests"]
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# review_configs = interrupt_value.get("review_configs", {}) # unused
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decisions: List[Dict[str, Any]] = []
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for idx, action in enumerate(action_requests):
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name = action.get("name", "<unknown>")
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args = action.get("args", {})
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description = action.get("description", "")
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print(f"\nAction {idx + 1}: {name}")
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if description:
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print(f"Description: {description}")
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print(f"Arguments: {args}")
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while True:
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choice = input("Decision (a=approve, r=reject): ").strip().lower()
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if choice == "a":
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decisions.append({"type": "approve"})
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break
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elif choice == "r":
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reason = input("Reason for rejection: ")
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decisions.append({"type": "reject", "message": reason})
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break
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else:
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print("Invalid input. Please enter 'a' or 'r'.")
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result = agent.invoke(
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Command(resume={"decisions": decisions}),
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config=config,
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)
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final_content = result["messages"][-1]["content"]
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print("\nAgent response:")
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print(final_content)
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if __name__ == "__main__":
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main()
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